ArticleScientific reports2014
Critical limitations of consensus clustering in class discovery.
Article in Scientific reports, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 217 papers.
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Who cites it
217 citing papers in PubMed.
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- Risk stratification of ST-segment elevation myocardial infarction (STEMI) patients using machine learning based on lipid profiles.Lipids in health and disease · 2021Trial
- Potential diagnostic role of urinary exosomal microRNAs in immunoglobulin A nephropathy: A case-control study.World journal of nephrology · 2026Article
- Machine perfusion distribution across clinical phenotypes in kidney transplantation: A national cohort study using unsupervised clustering.World journal of transplantation · 2026Article
- Multimodal data-driven clustering analysis of heart failure patients identifies two distinct patterns of heart failure with preserved ejection fraction: Results from the PACIFIC-PRESERVED study.Nature communications · 2026Observational
- Ensemble clustering: A practical tutorial.Behavior research methods · 2026Article
- Thrombus Metabolism-Based Molecular Subtyping for Prognostic Risk Stratification in Acute Ischemic Stroke: A Preliminary Study.CNS neuroscience & therapeutics · 2026Article
- Multimodal Factor Analysis Reveals Five Robust Phenotypes of Healthy Aging in a Russian Population Cohort.Biomedicines · 2026Article
- A Ponatinib-Associated Transcriptomic Signature: Implications for Cardiovascular Toxicity.International journal of molecular sciences · 2026Article
- A macrophage MHC-I axis links intratumoral heterogeneity to terminal CD8NPJ precision oncology · 2026Article
- SpaNiche: spatial niche analysis to explore colocalization patterns and cellular interactions in spatial transcriptomics data.Genome biology · 2026Article
- Extracellular matrix-driven patient stratification and network modeling reveal distinct molecular grades with potential clinical implications.NPJ systems biology and applications · 2026Article
- A gene interaction network-based molecular taxonomy of dilated cardiomyopathy highlighting immune and inflammatory heterogeneity.Clinical and experimental medicine · 2026Article
- Defining PI3K Signaling as a Key Regulator of Molecular Heterogeneity and Inflammation in Glomerular Lupus Nephritis.Immune network · 2026Article
- Multi-omics profiling identifies ectopic olfactory receptors as putative drivers of tumor progression and prognostic indicators in clear cell renal cell carcinoma.Clinical and experimental medicine · 2026Article
- Dynamic coactivation patterns during repetitive negative thinking: A cross-sectional fMRI study.Psychological medicine · 2026Article
- Molecular subtyping and immune microenvironment heterogeneity in pediatric influenza-associated prolonged multiple organ dysfunction syndrome.Translational pediatrics · 2026Article
- Unbiased clustering of acute-on-chronic liver failure patients using machine learning in a real-world ICU cohort.Nature communications · 2026Article
- Anterior insular co-activation patterns associated with stress markers in chronic primary pain.Brain communications · 2026Article
157 more citing papers are in PubMed but not listed here.
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3 authors.
Funding
Abstract
Consensus clustering (CC) has been adopted for unsupervised class discovery in many genomic studies. It calculates how frequently two samples are grouped together in repeated clustering runs, and uses the resulting pairwise "consensus rates" for visual demonstration that clusters exist, for comparing cluster stability, and for estimating the optimal cluster number (K). However, the sensitivity and specificity of CC have not been systemically assessed. Through simulations we find that CC is able to divide randomly generated unimodal data into apparently stable clusters for a range of K, essentially reporting chance partitions of cluster-less data. For data with known structure, the common implementations of CC perform poorly in identifying the true K. These results suggest that CC should be applied and interpreted with caution. We found that a new metric based on CC, the proportion of ambiguously clustered pairs (PAC), infers K equally or more reliably than similar methods in simulated data with known K. Our overall approach involves the use of realistic null distributions based on the observed gene-gene correlation structure in a given study, and the implementation of PAC to more accurately estimate K. We discuss the strength of our approach in the context of other ensemble-based methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.